The summer term is approximately four weeks, starting in early July

• CS 5010: Programming and Systems for Data Science | Credits: 3
  • The objective of this course is to introduce basic data analysis techniques including data analysis at scale, in the context of real-world domains such as bioinformatics, public health, marketing, security, etc. For the purpose of facilitating data manipulation and analysis, students will be introduced to essential programming techniques in Python, an increasingly prominent language for data science and "big data" manipulation.
• STAT 6021: Linear Models for Data Science | Credits: 3
  • An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components.  The primary software is R.

• CS 5012: Foundations of Computer Science | Credits: 3
  • Provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency for non-CS, non-CpE students. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).
• SYS 6018: Data Mining | Credits: 3
  • Data mining describes approaches to turning data into information. Rather than the more typical deductive strategy of building models using known principles, data mining uses inductive approaches to discover the appropriate models. These models describe a relationship between a system's response and a set of factors or predictor variables. Data mining in this context provides a formal basis for machine learning and knowledge discovery. This course investigates the construction of empirical models from data mining for systems with both discrete and continuous valued responses. It covers both estimation and classification, and explores both practical and theoretical aspects of data mining. 
• DS 6014: Bayesian Machine Learning | 3
  • Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference.
• DS 6001: Practice and Application of Data Science I Credits: 2
  • This course covers the practice of data science practice, including communication, exploratory data analysis, and visualization.  Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data.  Case studies will explore the impact of data science across different domains. 
• DS 6002: Ethics of Big Data | Credits: 2
  • This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives.
• DS 6011: Data Science Capstone Project Work I | Credits: 1
  • This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects. 

• SYS 6016: Machine Learning | Credits: 3
  • A graduate-level course on machine learning techniques and applications with emphasis on their application to systems engineering. Topics include: Bayesian learning, evolutionary algorithms, instance-based learning, reinforcement learning, and neural networks. Students are required to have sufficient computational background to complete several substantive programming assignments.
• DS 6003: Practice and Application of Data Science II | Credits: 1
  • This course covers the practice of data science practice, including communication, exploratory data analysis, and visualization.  Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data.  Students will use their capstone projects to explore the impact of data science on that domain. 
• DS 6013: Data Science Capstone Project Work II | Credits: 2
  • This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.
• Elective 1 (5000-level or higher, at least 3 credit hours)*
• Elective 2 (5000-level or higher, at least 3 credit hours)*

*Electives can only be taken in the Spring term, up to a total of 6 credit hours. Additional credit hours will require further consideration.

You will choose your electives in consultation with the program director. There are a variety of electives available, including (but not limited) to those suggested in the Graduate Record. Students are required to take 6 credits of elective courses, which occur in the spring term.

Elective courses must be at the 5000 level or higher to count for elective credit in program unless further approval is obtained.

• CS 6160: Theory of Computation
• CS 6444: Parallel Computing
• CS 6501: Special Topics in Computer Science (Topics approved by the DSI). Examples of accepted topics are: Text Mining, Cloud Computing, Defense Against the Dark Arts, Vision & Language.
• CS 6750: Database Systems

• DS 6559: Biomedical Cloud Computing Seminar 

• ECON 8720: Time Series Econometrics

• ECON 7720: Econometrics II
• EVSC 7070: Advanced Use of Geographical Information Systems
• GCOM 7240: Advanced Quantitative Analysis
• PHS 5705: Recent Advances in Public Health Genomics
• PHS 7310: Clinical Trials Methodology
• PSYC 5720: Fundamentals of Item Response Theory
• PSYC 7760: Introduction to Applied Multivariate Methods
• SARC 5400: Data Visualization
• STAT 6250: Longitudinal Data Analysis
• STAT 6260: Categorical Data Analysis
• SYS 6023: Cognitive Systems Engineering
• SYS 6050: Risk Analysis
• SYS 6582: Selected Topics in Systems Engineering (Topics approved by the DSI). Examples of accepted topics are: Reinforcement learning, User Experience Design, Sensors & Perception.
• SYS 7001: System and Decision Sciences

Other electives are possible, depending on available courses and as approved by the Program Director.